Dataset Information

Sports activity patterns and cardio-metabolic health over time among adults in Germany: Results of a nationwide 12-year follow-up study.

ABSTRACT:

Background

Physical activity is favorable for health, and vigorous sports activity is particularly beneficial. This study investigates the association between changes in sports participation patterns over time and cardio-metabolic and self-perceived health outcomes.

Methods

Data from 3752 adults (18-79 years of age) who participated in 2 national health interview and examination surveys in 1997-1999 and 2008-2011 were included, with a mean follow-up time of about 12 years. A change in self-reported sports activity was analyzed with respect to the incidence of type 2 diabetes, coronary heart disease (CHD), hypertension, obesity, dyslipidemia, metabolic syndrome, and poor self-perceived health. Participants with pre-existing disease or risk factor of interest at baseline were excluded from the analysis. Being sufficiently active in sports was specified as doing sports for at least 1-2 h per week, and 4 activity categories were defined: 1) inactive at both time points (inactive-inactive), 2) inactive at baseline and active at follow-up (inactive-active), 3) active at baseline and inactive at follow-up (active-inactive), and 4) active at both time points (active-active). Associations between sports activity engagement and health outcomes were estimated by logistic regression models with different stages of adjustments.

Results

Not engaging in any regular sports activity at both time points (inactive-inactive) was associated with higher rates of type 2 diabetes (odds ratio (OR) = 1.82, 95% confidence interval (95%CI): 1.08-3.08), CHD (OR = 1.82, 95%CI: 1.16-2.84), hypertension (OR = 1.36, 95%CI: 1.03-1.81), metabolic syndrome (OR = 1.58, 95%CI: 1.08-2.32), and poor self-perceived health (OR = 2.54, 95%CI: 1.83-3.53) compared to doing regular sports for a minimum of 1-2 h per week over time (active-active). In case of change from inactivity to any regular sports activity (inactive-active), the rate of risk factor occurrence was not statistically different from the active-active reference group except for poor self-perceived health, but it was higher for type 2 diabetes (OR = 2.15, 95%CI: 1.12-4.14) and CHD (OR = 1.77, 95%CI: 1.03-3.03). Being active at baseline but inactive at follow-up (active-inactive) was not associated with higher disease incidence of type 2 diabetes (OR = 0.70, 95%CI: 0.25-1.97) or CHD (OR = 1.20, 95%CI: 0.49-2.99), but was associated with higher rates of hypertension (OR = 1.61, 95%CI: 1.11-2.34), obesity (OR = 2.34, 95%CI: 1.53-3.57), metabolic syndrome (OR = 1.70, 95%CI: 1.11-2.63), and poor self-perceived health (OR = 2.16, 95%CI: 1.53-3.07) at follow-up.

Conclusion

Even a low weekly quantity (1-2 h) of regular sports activity is partly associated with health benefits. Being formerly but not currently active was not associated with an increased disease incidence, but was associated with a higher risk-factor development compared to the reference group (active-active). Becoming active was preventive for risk-factor development but was not preventive for disease incidence, which probably means that the health benefits from sports activity are not sustainable and disease incidence is only shifted to a later period in life. For this reason, the promotion of and commitment to regular sports activity should be addressed as early as possible over the lifespan to achieve the best health benefits.

SUBMITTER: Gabrys L

PROVIDER: S-EPMC8343057 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:To assess longer-term patterns of glaucoma medication adherence and identify whether patterns established during the first year of medication use persist during 3 subsequent years of follow-up.Retrospective, longitudinal cohort analysis.Beneficiaries aged ?40 years who were enrolled in a United States (US)-managed care plan for ?7 years between 2001 and 2012 and newly diagnosed and treated for open-angle glaucoma.For each enrollee, we quantified medication adherence using the medication possession ratio. Group-based trajectory modeling (GBTM) was applied to identify patterns of adherence for 1 and 4 years of follow-up. The percent of beneficiaries who remained in the same trajectory group in the 1- and 4-year models was tabulated to evaluate group stability. Factors impacting adherence at 1 and 4 years were identified using regression analyses.Patterns of glaucoma medication adherence.Of the 1234 eligible beneficiaries, GBTM identified 5 distinct glaucoma medication adherence patterns in both the 1-year and 4-year follow-up periods. These groups were as follows: (1) never adherent after their index prescription fill (7.5% and 15.6% of persons in the 1- and 4-year models, respectively); (2) persistently very poor adherence (14.9% and 23.4% of persons in the 1- and 4-year models, respectively); (3) declining adherence (9.5% and 9.1% of persons in the 1- and 4-year models, respectively); (4) persistently moderate adherence (48.1% and 37.0% of persons in the 1- and 4-year models, respectively); and (5) persistently good adherence (20.0% and 15.0% of persons in the 1- and 4-year models, respectively). More than 90% of beneficiaries in the 4 groups with the worst and best adherence patterns (groups 1, 2, 3, 5) maintained their patterns from their first year throughout their 4 years of follow-up. Those with persistently moderate adherence (group 4), the largest group, were most likely to change groups from 1 to 4 years of follow-up. Persons with the best adherence over 4 years were more likely to be white, to be older, to earn >$60 000/year, and to have more eye care visits (P < 0.05 for all comparisons). Those with a higher initial copayment cost had lower adherence rates (? = -0.06/dollar, P = 0.03).For most patients who were newly prescribed glaucoma medications, adherence patterns observed in the first year of treatment reflect adherence patterns over the subsequent 3 years. Investing resources in both identifying and helping patients with suboptimal adherence patterns over the first year may have a large impact on longer-term adherence.

Project description:BackgroundThe objective of the study was to determine whether plasma biomarkers of kidney injury improve the prediction of diabetic kidney disease (DKD) in adults with type 1 diabetes (T1D) over a period of 12 years.MethodsParticipants (n = 527, 53% females) in the Coronary Artery Calcification in T1D (CACTI) Study were examined during 2002-04, at a mean (± standard deviation) age of 39.6 ± 9.0 years with 24.8 years as the median duration of diabetes. Urine albumin-to-creatinine (ACR) and estimated glomerular filtration rate (eGFR) by CKD-EPI (chronic kidney disease epidemiology collaboration) creatinine were measured at the baseline and after mean follow-up of 12.1 ± 1.5 years. Albuminuria was defined as ACR ≥30 mg/g and impaired GFR as eGFR <60 mL/min/1.73 m2. Kidney injury biomarkers (Meso Scale Diagnostics) were measured on stored baseline plasma samples. A principal component analysis (PCA) identified two components: (i) kidney injury molecule-1, calbindin, osteoactivin, trefoil factor 3 and vascular endothelial growth factor; and (ii) β-2 microglobulin, cystatin C, neutrophil gelatinase-associated lipocalin and osteopontin that were used in the multivariable regression analyses.ResultsComponent 2 of the PCA was associated with increase in log modulus ACR [β ± standard error (SE): 0.16 ± 0.07, P = 0.02] and eGFR (β ± SE: -2.56 ± 0.97, P = 0.009) over a period of 12 years after adjusting for traditional risk factors (age, sex, HbA1c, low-density lipoprotein cholesterol and systolic blood pressure and baseline eGFR/baseline ACR). Only Component 2 of the PCA was associated with incident-impaired GFR (odds ratio 2.08, 95% confidence interval 1.18-3.67, P = 0.01), adjusting for traditional risk factors. The addition of Component 2 to traditional risk factors significantly improved C-statistics and net-reclassification improvement for incident-impaired GFR (ΔAUC: 0.02 ± 0.01, P = 0.049, and 29% non-events correctly reclassified, P < 0.0001).ConclusionsPlasma kidney injury biomarkers can help predict development of DKD in T1D.